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510(k) Data Aggregation
(165 days)
FEops HEARTguide™ ALPACA enables visualization and measurement of structures of the heart and vessels for preprocedural planning and sizing of structural heart interventions.
To facilitate the above, FEops HEARTguide™ ALPACA provides general functionality such as:
- Segmentation of cardiovascular structures
- Visualization and image reconstruction techniques: 2D review, MPR
- Measurement and annotation tools
- Reporting tools
FEops HEARTguide™ ALPACA also allows visualization of output generated by other medical device software (e.g., FEops HEARTguide™ Simulation Application cleared as K214066).
The results are intended to be used by qualified clinicians in conjunction with the patient's clinical history, symptoms, and other preprocedural evaluations, as well as the clinician's professional judgment.
FEops HEARTguide™ ALPACA is not intended to replace the implant device instructions for use for final LAAO and TAVI device selection and placement.
FEops HEARTguide™ ALPACA enables visualization and measurement of structures of the heart and vessels for preprocedural planning and sizing of structural heart interventions.
The software is used in a service-based business model: the customer (clinician) provides the necessary input data, FEops prepares the anatomical analysis, and delivers the results to the customer.
The results of the anatomical analysis are provided to the clinician via FEops HEART guide™ ALPACA's web application. They are available in a PDF report and as interactive 3D and DICOM MPR visualizations. The web application is intended to be used by clinicians to review the results as well as to create additional landmarks and related measurements, if needed.
The FEops HEARTguide™ ALPACA device underwent non-clinical performance testing to demonstrate its substantial equivalence to a predicate device (3mensio Workstation/3mensio Structural Heart/3mensio Vascular, K153736) for preprocedural planning and sizing of structural heart interventions. The study details are provided below.
1. Acceptance Criteria and Reported Device Performance
The device's performance was evaluated for two primary applications: Left Atrial Appendage Occlusion (LAAO) and Transcatheter Aortic Valve Implantation (TAVI) procedures, focusing on quantitative measurements and segmentation accuracy.
Table: Acceptance Criteria and Reported Device Performance
| Metric | Performance Goal (Acceptance Criteria) | Reported Device Performance (Semi-Automatic Output) | Reported Device Performance (Fully Automatic Output) |
|---|---|---|---|
| LAAO Procedures | |||
| LAA Landing Zone Mean Diameter Difference (Bland-Altman) | Lower CI on inferior LoA within ±18% | Lower CI on inferior LoA: -10.5% | Lower CI on inferior LoA: -14.4% |
| Upper CI on superior LoA within ±18% | Upper CI on superior LoA: 13.2% | Upper CI on superior LoA: 22.6% (Failed for Fully Automatic) | |
| LAA Region Segmentation (Dice Score) | (Implicitly high accuracy for clinical utility) | Mean: 0.98 ± 0.01 | |
| Minimum: 0.95 | Mean: 0.93 ± 0.04 | ||
| Minimum: 0.83 | |||
| TAVI Procedures | |||
| Aortic Annulus Perimeter-Based Diameter Difference (Bland-Altman) | Lower CI on inferior LoA within ±10% | Lower CI on inferior LoA: -4.3% | Not applicable (Manual measurement required) |
| Upper CI on superior LoA within ±10% | Upper CI on superior LoA: 5.3% | Not applicable (Manual measurement required) | |
| Aortic Root Segmentation (Dice Score) | (Implicitly high accuracy for clinical utility) | Mean: 0.97 ± 0.01 | |
| Minimum: 0.92 | Mean: 0.96 ± 0.01 | ||
| Minimum: 0.92 |
Note on LAAO Fully Automatic Output: While the semi-automatic output met the ±18% performance goal for LAAO landing zone mean diameter, the fully automatic output's upper CI on superior LoA (22.6%) exceeded the 18% threshold, indicating it did not meet the performance goal. However, the overall submission focuses on the semi-automatic workflow which incorporates human supervision.
2. Sample Sizes and Data Provenance
- Test Set Sample Size:
- LAAO: 35 representative retrospective cases.
- TAVI: 35 representative retrospective cases.
- Data Provenance: The data consisted of "Recent datasets representative for the intended population," covering different CT manufacturers, imaging parameters (e.g., slice thickness), and regions. The text does not specify the country of origin but implies clinical relevance for the intended user base. All data used for testing were retrospective and specifically excluded any datasets used for training the AI models.
3. Number of Experts and Qualifications for Ground Truth
The document states that the ground truth was "manually annotated data." It does not explicitly specify the number of experts or their qualifications (e.g., radiologist with 10 years of experience). However, the context of regulatory submission for medical devices strongly implies that such manual annotations would be performed by qualified medical professionals.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (such as 2+1 or 3+1) for establishing the ground truth. It states that the ground truth was "manually annotated data." Given that the process for "semi-automatic output" involves "human supervision and a quality check by a FEops Case analyst," it suggests an internal review process, but not a formal multi-reader adjudication for the initial ground truth establishment.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance was reported in the provided text. The evaluation focused on the accuracy of the device's measurements and segmentations against manually established ground truth, rather than directly assessing human reader improvement. The "semi-automatic" workflow inherently describes a human-in-the-loop process where human supervision and quality checks are applied after AI segmentation.
6. Standalone (Algorithm Only) Performance
Standalone (algorithm only) performance was evaluated for:
- LAAO: "Fully automatic output" for mean diameter of the landing zone and dice score for segmentation.
- TAVI: "Fully automatic output" for dice score on aortic root segmentation.
- For TAVI perimeter-based diameter, the document explicitly states, "there is no automatically calculated perimeter-based diameter of the aortic annulus, as the algorithm only identifies the annular plane, and the measurement itself requires a manual action." This implies that a fully standalone measurement for this metric is not applicable.
7. Type of Ground Truth Used
The ground truth used for both LAAO and TAVI studies was expert manual annotation of imaging data, referred to as "manually annotated data."
8. Sample Size for the Training Set
The document explicitly states that the test datasets were "No datasets were included that were used for training the AI models." It does not provide the specific sample size for the training set used for the AI models.
9. How the Ground Truth for the Training Set Was Established
The document does not provide details on how the ground truth for the training set was established. It only mentions that the test set cases were not used for training.
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